I think much of this is overhyped as well, but I disagree that whether modeling human brain structure is relevant. The term "neural network" has historical baggage (responsible for some of the hype), but these days refers to a class of mathematical approaches with only historical connection to "neurons". Those can be interesting on their own for AI purposes, and imo accurate modeling of the human brian, while interesting for neuroscience research, is not necessarily the way forward for AI research.

My use of modeling was indeed inappropriate. What I meant is the working principle of cortical neural networks. The artificial model would then be a kind of proof of concept.

Regarding you other point, it is a matter of research strategy. I think that the path trying understanding the working principle of real cortical neural network is the shortest path to AI. My impression is that the other path which is to play around with artificial neural networks is too hazardous.

We can make a parallel with learning to fly. We are in a similar situation regarding how the brain works and AI. Understanding how birds fly require a true research. People seems to simply focus on flapping while this is not the real working principle of flight.

I see there a strong analogy with artificial neural network. The most relevant properties of cortical neural networks are ignored.

With flight the proof condition of mastering it was obvious. With AI, it is less obvious. I would be glad to hear suggestions. Face recognition is the most difficult condition because this process is the end product of many prior processes like 3D perception and feature extractions. My current impression is that talk decoding would be a much better candidate. Siri shows the potential impact of such AI product. At least the turing test would be a direct match.

I guess I take the opposite suggestion from the flight example: we succeeded in flying once we started focusing more on the physical/mathematical research of aerodynamics and lift, and less on attempting to mimic biology, in copying the biomechanics of bird wings. We ended up producing something that flies, but not in exactly the way that birds fly. That's what I tend to view as the better route for AI as well: instead of trying to copy the details of how a brain works, focus more on first-principles mathematical/logical principles of inference, whether they're symbolic ones (e.g. theorem-proving) or statistical ones (Bayesian networks, etc.).

Admittedly this is a big area of disagreement both within and outside the field.